AI Systems · core
Open-Weight Models
Model cards, licenses, weights, tokenizers, chat templates, adapters, provenance, and reproducible packaging.
Mental model
Open weights provide inspectable parameters, not automatically open data or unrestricted rights. Selection must include license, provenance, tokenizer, template, eval, and hardware fit.
How to study Open-Weight Models
Begin by restating the mental model in your own words, then connect it to a concrete system you have built or operated. Name the mechanism, the constraint it addresses, and the trade-off it introduces. Use The Llama 3 Herd of Models, DeepSeek-V3 Technical Report, Rethinking open source generative AI: open-washing and the EU AI Act (FAccT '24) to check details, but close the source before writing your explanation. Retrieval is the learning step; rereading is only preparation.
Next, compare Open-Weight Models with Model Quantization, Multimodal Models. Ask what changes in correctness, latency, resource use, operability, and failure recovery. Complete Design exercise: Open-Weight Models and preserve the command, input, output, and one failed attempt as evidence. Finish by explaining the idea without jargon to someone who has not studied the track.
Proof of understanding
- Explain the mechanism from first principles and identify the state it reads or changes.
- Give one situation where the concept is the right choice and one where it is not.
- Predict a realistic failure mode before running the drill, then compare the prediction with evidence.
- Connect the result to a roadmap or build artifact instead of treating the concept as isolated trivia.
Learn from primary sources
Practice and explain it back
Design exercise: Open-Weight Models
Model cards, licenses, weights, tokenizers, chat templates, adapters, provenance, and reproducible packaging. Implement designOutline() returning non-empty values for: license, provenance, runtimeFit. Each value must name a concrete mechanism or decision.
Expected evidence: A design outline with license, provenance, runtimeFit plus an explicit failure mode or trade-off.
Open the interactive drill →Review prompts
- "Open weights" is not "open source". Name what you still do not get, and the one packaging detail that breaks deployments quietly.
Build evidence
Synthesize: AI Models & Training
Move from transformer foundations through pre-training, fine-tuning, post-training, compression, and evaluation. Produce one working system, benchmark, or evidence-backed design that integrates the path.
- Implements or precisely models the core mechanisms from all three milestones
- Includes at least one injected failure or adversarial case and demonstrates recovery
- Reports quality, latency, resource, reliability, or usability measurements relevant to the domain
- Ships a concise architecture note explaining decisions, trade-offs, and remaining risks